New Horizons of Science, Technology and Culture Vol. 9

Workplace Transformation in the Digital Era: Proposing a Conceptual Model

Partha Naskar — 2026-03-14

In an era of rapid technological advancement and cut-throat global competition, digital transformation has become quintessential. Undoubtedly, digital technologies accelerate productivity while simultaneously changing the employee roles, working experiences, and work sphere. Drawing from extensive literature and validated expert opinion, the chapter introduces the ‘Digital Workplace Transformation Assessment (DWTA)’ scale for millennial collaborative workplaces, aligning digital transformation, self-determination, organisational behaviour and future workforce. The study emphasises prime domains comprising Digital transformation, Skill Orientation, Self-determination, Inclusive workplaces and Organisational behaviour with structured sub-domains and items. Methodology of the study collaborates expert evaluation, statistical item analysis and scale reliability with a 0.936 value demonstrating first-rate reliability and robustness of the measurement scale. The DTWA scale provides the backbone, combining technology and organisational behaviour towards formulating an integrated theoretical framework. Findings of the chapter underscore in formulating comprehensive ‘Futuristic Digital Transformation Workplace Framework’ for evaluating and fostering inclusive, culturally digital intelligent workplaces. The chapter showcases a conceptual model which aids as a dynamic tool for future HR professionals and policymakers to build a responsive and resilient, transformative digital work sphere equipped for unforeseen global challenges.

Bridging the Gap: Consumer Perceptions and Industry Practices of Sustainable Materials in Indian Interior Design

Anjali Marwah — 2026-03-14

Sustainability in interior design is fast gaining importance in India as a consequence of environmental concerns, changing consumer values and motivating incentives imposed by various regulatory bodies. This study evaluates Indian consumers’ willingness to adopt sustainable materials over conventional alternatives and examines consumer perceptions alongside industry practices within a rapidly urbanising and environmentally stressed context.

A mixed-method approach was applied, combining quantitative survey data from 450 urban consumers with qualitative information from 40 industry professionals, designers, suppliers and contractors. The descriptive statistics, cross-tabulation, and chi-square were used to analyse quantitative data and multivariate logistic regression was used to evaluate predictors of demand to purchase sustainable materials. Findings reveal the high conceptual awareness of sustainability (86%) but low ability to identify certified sustainable materials (54%). The average of 38.5 years means operating adults who were actively involved in the home improvement or renovation decisions. Consumer preference is notably strong for low-VOC paints (83% due to lower VOCs); bamboo (78% due to low carbon emissions and high water absorbency); natural fiber textiles (70% due to durability, recyclability and biodegradability); however, industry application remains significantly below for bamboo (55%) and natural fibers (48% due to cost barriers and instability of supply chain, undefined certification). Low-VOC paints have the highest correlation between consumer demand and industry pickup for their availability because of clear regulations and availability in the market. Willingness-to-pay analysis indicates that 56% of consumers are willing to invest in premium sustainable products; education, environmental concern and experience were found to be important predictors. According to industry stakeholders, cost, inconsistent supply and insufficient client awareness continue to be barriers. This study concludes that to address the perception-practice gap, it is important to have stronger certification frameworks, improved supply networks, policy incentives, and targeted educational interventions in order to mainstream sustainable materials in interior design in India.

A Synthetic CAD-to-SEM Pipeline for Design-Based Semiconductor Defect Detection Using Structural Similarity Analysis

Balachandar Jeganathan — 2026-03-14

The growing complexity of semiconductor manufacturing at advanced technology nodes has intensified the need for robust, automated defect inspection methods. Design-based inspection, which compares scanning electron microscope (SEM) images of fabricated wafers against their original computer-aided design (CAD) layouts, offers a powerful approach for detecting both systematic and random defects. However, the development and benchmarking of such inspection algorithms is severely constrained by the scarcity of labelled SEM defect datasets, which are expensive to produce and tightly guarded as proprietary by fabrication facilities. This chapter presents an end-to-end, open-source pipeline for generating synthetic paired CAD and SEM image datasets with controllable, ground-truth-labelled defects, and for performing automated defect detection through structural similarity (SSIM) analysis. The pipeline comprises five modular stages: (1) synthetic layout generation in industry-standard OASIS format, (2) paired CAD and SEM-like image rendering with physically motivated degradation models, (3) configurable synthetic defect injection, (4) phase-correlation-based image alignment followed by local SSIM computation and morphological post-processing for defect mask extraction, and (5) aggregate scoring and ranking of inspection sites by defect severity. The paired image rendering generates, for each inspection site, a clean binary CAD image and a SEM-like image with sequential physically motivated degradations simulating realistic electron microscopy effects. The defect injection module selectively modifies a configurable fraction of SEM images (default 10%) to introduce synthetic defects. The detection stage processes each matched pair of CAD and SEM images through four substeps: alignment, similarity mapping, thresholding, and morphological cleanup. Experimental results on a generated dataset of 200 image pairs demonstrate that the pipeline achieves a detection F1-score of 0.93 under moderate noise conditions and degrades gracefully as imaging noise increases. The fully reproducible, configurable nature of this toolkit makes it suitable for algorithm benchmarking, machine learning model pre-training, threshold optimisation studies, and educational demonstrations of semiconductor inspection concepts.

Reframing Vulvar and Perineal Dermatoses across the Female Life Course: Integrating Clinical Insight, Biological Innovation, and Sociocultural Context

Mariam Sherif Mohamed — 2026-03-14

Vulvar and perineal dermatoses comprise a heterogeneous spectrum of inflammatory, autoimmune, infectious, and neoplastic conditions that affect women across all stages of the female life course. Despite their frequency and clinical impact, these disorders remain under-recognised and are frequently misdiagnosed due to overlapping morphologic patterns, symptom non-specificity, sociocultural barriers to genital examination, and limited formal training in vulvar disease. Diagnostic delay contributes to chronic symptoms, architectural change, sexual dysfunction, and, in selected conditions, malignant transformation.

This chapter adopts a life-course framework to examine how physiologic transitions, including hormonal fluctuation, immune modulation, epithelial barrier dynamics, microbiome variation, and evolving environmental exposures, can shape disease susceptibility and phenotype expression from infancy through postmenopause. Major inflammatory dermatoses, particularly lichen sclerosus, lichen planus, psoriasis, and contact dermatitis, are analysed with emphasis on structured diagnostic reasoning, clinicopathologic correlation, appropriate indications for biopsy and patch testing, longitudinal management strategies, and principles of surveillance.

In parallel, the chapter integrates emerging scientific and technological advances that are reshaping the field, including developments in immunopathogenesis, molecular characterisation, microbiome research, non-invasive imaging, digital health tools, and artificial intelligence–assisted diagnostic approaches. Cultural and healthcare-system factors influencing presentation, access, and outcomes are also examined, with attention to disparities and variation across populations.

By synthesising contemporary evidence within an interdisciplinary yet clinically grounded framework, this chapter bridges fundamental biology and specialist practice while outlining future directions for research, innovation, and improved care in vulvar and perineal dermatoses.

A Customised LSTM-Based Deep Learning Framework for Transformer Predictive Maintenance: Performance Analysis

G.V.S.S.N. Srirama Sarma — 2026-03-14

Transformers are critical and costly components of power systems whose health deteriorates over time due to factors such as poor cooling and heavy loading. Consequently, predictive maintenance is emerging as an effective alternative to conventional corrective maintenance, enabling continuous monitoring and early fault detection.

To enhance the effectiveness of predictive maintenance for power transformers under limited Dissolved Gas Analysis (DGA) data conditions, this study proposes a customised Long Short-Term Memory (C-LSTM) deep learning model. The developed C-LSTM architecture is specifically designed to address the limitations of conventional LSTM networks, which often exhibit higher classification error rates when trained on small datasets and may underperform compared to traditional machine learning approaches.

A comprehensive performance evaluation was conducted by comparing the proposed C-LSTM model with several well-established traditional machine learning algorithms using multiple metrics, including validation accuracy, test accuracy, precision, recall, and F1-score. Additionally, the diagnostic capability of the model was rigorously assessed across seven transformer fault categories, including low- and high-energy discharges, partial discharge, electrical and thermal faults, and low-, medium-, and high-temperature thermal faults.

The experimental results demonstrate the superior classification and diagnostic performance of the proposed C-LSTM model, achieving a validation accuracy of 100% and a test accuracy of 98.57%, significantly outperforming conventional machine learning techniques. These findings confirm that the proposed C-LSTM framework offers a robust and reliable solution for transformer fault diagnosis and predictive maintenance, particularly in scenarios characterised by scarce DGA datasets.

Active Phase Stabilisation in a Plasma Resonator Using Feedback Control and Auxiliary Scalar-like Coupling

Derrick Covington — 2026-03-14

Maintaining coherence in resonant plasma and hybrid quantum systems remains a central challenge due to phase drift, environmental coupling, and entropy production. In this work, we investigate an active coherence locking framework for a plasma-based resonator using feedback-mediated phase control, auxiliary scalar field coupling, and entropy-aware regulation. The present study focuses explicitly on classical phase coherence, defined here as sustained phase synchronisation between coupled oscillatory degrees of freedom, while treating quantum coherence as a long-term target rather than a demonstrated property of the modelled system.

A phenomenological scalar field is introduced as an auxiliary control channel that mediates phase alignment between resonant plasma modes, while entropy flow is monitored and regulated to suppress destabilising fluctuations. Using time-resolved numerical simulations, we demonstrate that active feedback can rapidly drive the system into a stable phase-locked regime and maintain coherence within defined operational bounds. A critical instability threshold (“tearing threshold”) is identified, beyond which feedback control fails, and coherence degrades.

While the underlying plasma dynamics are treated in a classical or semiclassical regime, the control architecture is motivated by concepts from quantum feedback and coherence preservation. The results establish a classical coherence-stabilisation platform that may serve as a precursor to experimentally testable strategies for coherence preservation in more explicitly quantum systems. This work, therefore, provides a controlled bridge between classical resonant stabilisation and future quantum-coherent implementations.

Urinary Electrolyte Patterns among Populations of Grand Sido and Kouh-Est: A Comparative Study

Abdelsalam Hassan Gogo — 2026-03-14

Background: Urinary electrolytes play a key role in assessing hydration status, metabolism and renal function. The term “urinary electrolytes” generally refers to the urinary concentrations of sodium, potassium, and chloride. In many parts of the world, particularly in sub-Saharan Africa, data on the composition and concentration of urinary electrolytes remain limited, especially in rural areas.

Aims: This study aimed to characterise and quantify the major urinary electrolytes (Na⁺, K⁺, Ca²⁺, Cl⁻) in two rural areas of southern Chad: Grand Sido and Kouh-Est.

Methods: A cross-sectional, descriptive, and analytical study was conducted among 783 volunteer participants aged 5–90 years residing in Grand Sido (n = 430) and Kouh-Est (n = 353). Morning urine samples were collected under aseptic conditions and transported to the laboratory in accordance with storage conditions. Urinary concentrations of sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺), and chloride (Cl⁻) were determined using standardised biochemical methods (colourimetric methods). Statistical analyses were performed using the chi-square test to compare electrolyte profiles between the two areas and across age groups, with a significance threshold set at p < 0.05.

Findings: This study highlights significant differences in urinary electrolyte profiles between Grand Sido and Kouh-Est, reflecting the influence of nutritional factors, access to drinking water, and local environmental conditions. The observed prevalence of electrolyte imbalances, particularly hyponatraemia and hypocalcaemia, suggests a state of nutritional and metabolic vulnerability within the studied populations. These disturbances may be associated with inadequate dietary intake, increased hydroelectrolytic losses, or the presence of chronic and endemic parasitic diseases, including renal disorders and schistosomiasis.

Conclusion: These findings provide a useful reference for assessing hydration status and renal function in these rural populations. They also offer an important basis for developing targeted nutrition, prevention, and public health strategies adapted to the specific context of southern Chad. Despite certain limitations, including the absence of systematic measurement of urinary creatinine and acid–base parameters and limited information on participants’ dietary habits, this study provides important preliminary data and a regional baseline that may guide future research in southern Chad.

Future-Proofing the Zimbabwe Prisons and Correctional Service: Strategic Foresight and Institutional Resilience in Correctional Systems

Moses Cyril Ngawaite Chihobvu — 2026-03-14

Contemporary correctional institutions are increasingly embedded within complex sociotechnical, environmental, and security systems that expose them to systemic shocks and deep uncertainty, including climate-linked disruptions and resource constraints. In Zimbabwe, the Zimbabwe Prisons and Correctional Service (ZPCS) exemplifies these systemic pressures, reflecting the broader structural and operational constraints characteristic of correctional institutions in resource-limited contexts.

This study examines the growing vulnerability of contemporary correctional systems to intersecting technological, environmental, security and governance disruptions, with particular reference to the Zimbabwe Prisons and Correctional Service. Although Zimbabwe has not yet experienced widespread radicalisation or organised gang violence within correctional institutions, regional developments and the increasing mobility of incarcerated populations highlight the need for anticipatory preparedness. These emerging risks are compounded by structural challenges within the correctional system, including dependence on firewood-based energy, climate-sensitive agricultural production and limited digital infrastructure, which collectively heighten institutional exposure to climate variability, resource insecurity and operational disruption. Adopting a qualitative and conceptual research design grounded in strategic foresight analysis, the study examines how correctional leadership can strengthen institutional resilience, anticipate future shocks and enhance long-term adaptability. The analysis draws on strategic foresight methodologies including contextual analysis and scenario-based reflections to examine emerging risks facing correctional governance. This chapter advances a strategic foresight perspective on correctional governance by proposing a resilience-oriented framework for anticipating and managing emerging disruptions in correctional systems. Guided by systems theory, organisational resilience theory and strategic foresight perspectives, the analysis integrates global governance priorities with the contextual realities of Zimbabwe’s correctional environment.

The study proposes an integrated resilience framework structured around five mutually reinforcing pillars: digital integration, climate-smart sustainability, energy transition, ideological threat preparedness and rehabilitative innovation. Through scenario-based reflection, the framework illustrates how these pillars can enhance adaptive capacity, mitigate emerging risks and balance custodial security with rehabilitative effectiveness. The chapter contributes to emerging debates on correctional governance by advancing a forward-looking model that shifts institutional responses from reactive crisis management toward proactive and adaptive transformation. Through emphasising foresight-driven leadership, strategic partnerships and cross-sector collaboration, the study positions correctional systems as critical actors in national security, social rehabilitation and sustainable development within an increasingly complex and uncertain global environment.